The genetics of life threatening diseases offer the possibility of fundamental insights into pathophysiology that can transform diagnosis and management. Genome wide genetic association studies (GWASs) of the past decade have identified DNA sequence genotypic relationships to disease phenotypes, usually without any accompanying insight into the incredibly complex biology that operates between genotype and disease risk. Our DVA Merit Award work has focused on the genetics of Systemic Lupus Erythematosus (SLE). There are now >50 established and published genetic associations and our recent results will raise this to >100 SLE risk loci. Associations without mechanisms are of very limited practical utility. Our present focus has become elucidating these mechanisms and we have made much progress at the IRF5 and ETS1 lupus risk loci. Using frequentist and Bayesian statistics along with the differences and similarities of the associated variants in the major human ancestries we generate Ancestry Informed Credible Sets (AICSs) of plausibly causal variants. The subsequent search for allele specific functional consequences for these variants is enormously aided by all of the work now underway characterizing the protein and RNA species that interact with chromatin. Using the dataset infrastructure now available and methods that identify DNA ligands, we have identified ZBTB3 and STAT1 as relatively specific AICS risk allele transcription factors for IRF5 and ETS1, respectively. We propose to focus on the important association in the STAT4 gene with SLE where STAT1, this time through its expression, again appears to be important. We have reduced the plausibly causal variants from 56 to only 4 variants in the 2nd and 4th introns of STAT4. We have results suggesting the astonishing possibility that HMGA1 binds with varying allele specificity to 3 of the 4 variants in the AICS for the STAT4 locus. HMGA1 acts as a chromatin scaffold influencing DNA looping and chromatin conformation. We (and others) have shown that STAT4 expression is altered by the risk haplotype. We have recent data showing that STAT1 expression is also associated with STAT4 alleles. In addition, we show association of the DNA binding sites of STAT1 with the 53 published SLE risk loci (p?10-10). With the strong association at STAT4 with SLE across all human ancestries (1.2<odds ratio (OR)<1.8), the STAT4 allele dependent expression of STAT4 and STAT1, the demonstration of allele specific binding of STAT1 at ETS1, the relationship of STAT1 to SLE risk loci, and the association of STAT4 with other inflammatory diseases (rheumatoid arthritis (RA), Sjgren's syndrome (SS), primary biliary cirrhosis (PBC), progressive systemic sclerosis (PSS), and type 1 diabetes (T1D)), we conclude that the STAT1-STAT4 locus has earned our concentrated effort. We will experimentally evaluate the relationship between the AICS variants (Aim 1) and STAT4 and STAT1 expression in the context of HMGA1 binding to the AICS, especially using luciferase expression vectors and with chromatin editing of the AICS variants. In addition, we will use chromatin editing strategies (CRISPR technologies) to establish the influence of allelic differences at the 4 plausibly causal variants on the expression of STAT1 and STAT4. We will identify the allele specific binding of STAT1 and STAT4 at SLE loci (Aim 2) using a standard protocol for chromatin immunoprecipitation followed by sequencing (ChIP-seq). Experiments will be performed in transformed B cell lines and in isolated B cells, both from patients and controls. These experiments will set the stage for future experiments in other cell types (various T cells, monocytes, dendritic cells) and for the exploration of mechanism in the other STAT1-STAT4 associated diseases: RA, SS, PBC, PSS, and T1D, emphasizing how important understanding the mechanism(s) will be for understanding human autoimmunity. This project will help illuminate the inside of a black box now existent between DNA variants in STAT4 and SLE disease expression and in the process provide insights, data, and new tools that have the potential to influence management and the development of therapeutics.